# imports
from keras.applications.resnet import ResNet50
from keras.models import Model, Input
from keras.layers import Dense, Conv2D, MaxPool2D, Dropout
from keras.layers import Flatten
from keras.applications.vgg16 import decode_predictions
from keras.models import Sequential
from keras.optimizers import Adam, Adamax

import glob
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
import tensorflow as tf

seed = 1
np.random.seed(seed)
tf.random.set_seed(seed)

# load data
X = []
Y = []

for filename in glob.glob('/content/drive/MyDrive/images/pretrained/training/anger/*.jpg'):
  im=Image.open(filename).convert('RGB')
  im=im.resize((224,224))
  arr = np.array(im)
  X.append(arr)
  Y.append(1) # anger class

for filename in glob.glob('/content/drive/MyDrive/images/pretrained/training/disgust/*.jpg'):
  im=Image.open(filename).convert('RGB')
  im=im.resize((224,224))
  arr = np.array(im)
  X.append(arr)
  Y.append(2) # disgust class

for filename in glob.glob('/content/drive/MyDrive/images/pretrained/training/fear/*.jpg'):
  im=Image.open(filename).convert('RGB')
  im=im.resize((224,224))
  arr = np.array(im)
  X.append(arr)
  Y.append(3) # fear class

for filename in glob.glob('/content/drive/MyDrive/images/pretrained/training/happy/*.jpg'):
  im=Image.open(filename).convert('RGB')
  im=im.resize((224,224))
  arr = np.array(im)
  X.append(arr)
  Y.append(4) # happy class

for filename in glob.glob('/content/drive/MyDrive/images/pretrained/training/neutral/*.jpg'):
  im=Image.open(filename).convert('RGB')
  im=im.resize((224,224))
  arr = np.array(im)
  X.append(arr)
  Y.append(5) # neutral class

for filename in glob.glob('/content/drive/MyDrive/images/pretrained/training/sad/*.jpg'):
  im=Image.open(filename).convert('RGB')
  im=im.resize((224,224))
  arr = np.array(im)
  X.append(arr)
  Y.append(6) # sad class

for filename in glob.glob('/content/drive/MyDrive/images/pretrained/training/surprised/*.jpg'):
  im=Image.open(filename).convert('RGB')
  im=im.resize((224,224))
  arr = np.array(im)
  X.append(arr)
  Y.append(7) # surprised class

# convert to np array
X = np.array(X)

# reshape 
X = X.reshape(X.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
X = X /255

# encode outputs
Y = np.array(Y)

# randomize the data set - numpy arrays
randomize = np.arange(len(X))
np.random.shuffle(randomize)
X = X[randomize]
Y = Y[randomize]

Y = tf.keras.utils.to_categorical(Y)
num_classes = Y.shape[1]

resnet = ResNet50(include_top=False, weights='imagenet', input_shape=(224,224,3))
output = resnet.layers[-1].output
output = tf.keras.layers.Flatten()(output)
resnet = Model(resnet.input, outputs=output)
for layer in resnet.layers:
    layer.trainable = False
resnet.summary()

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5
94773248/94765736 [==============================] - 1s 0us/step
Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            
__________________________________________________________________________________________________
conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    
__________________________________________________________________________________________________
conv1_conv (Conv2D)             (None, 112, 112, 64) 9472        conv1_pad[0][0]                  
__________________________________________________________________________________________________
conv1_bn (BatchNormalization)   (None, 112, 112, 64) 256         conv1_conv[0][0]                 
__________________________________________________________________________________________________
conv1_relu (Activation)         (None, 112, 112, 64) 0           conv1_bn[0][0]                   
__________________________________________________________________________________________________
pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           conv1_relu[0][0]                 
__________________________________________________________________________________________________
pool1_pool (MaxPooling2D)       (None, 56, 56, 64)   0           pool1_pad[0][0]                  
__________________________________________________________________________________________________
conv2_block1_1_conv (Conv2D)    (None, 56, 56, 64)   4160        pool1_pool[0][0]                 
__________________________________________________________________________________________________
conv2_block1_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block1_1_relu (Activation (None, 56, 56, 64)   0           conv2_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block1_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block1_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv2_block1_2_relu (Activation (None, 56, 56, 64)   0           conv2_block1_2_bn[0][0]          
__________________________________________________________________________________________________
conv2_block1_0_conv (Conv2D)    (None, 56, 56, 256)  16640       pool1_pool[0][0]                 
__________________________________________________________________________________________________
conv2_block1_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block1_2_relu[0][0]        
__________________________________________________________________________________________________
conv2_block1_0_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_0_conv[0][0]        
__________________________________________________________________________________________________
conv2_block1_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_3_conv[0][0]        
__________________________________________________________________________________________________
conv2_block1_add (Add)          (None, 56, 56, 256)  0           conv2_block1_0_bn[0][0]          
                                                                 conv2_block1_3_bn[0][0]          
__________________________________________________________________________________________________
conv2_block1_out (Activation)   (None, 56, 56, 256)  0           conv2_block1_add[0][0]           
__________________________________________________________________________________________________
conv2_block2_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block1_out[0][0]           
__________________________________________________________________________________________________
conv2_block2_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block2_1_relu (Activation (None, 56, 56, 64)   0           conv2_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block2_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block2_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv2_block2_2_relu (Activation (None, 56, 56, 64)   0           conv2_block2_2_bn[0][0]          
__________________________________________________________________________________________________
conv2_block2_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block2_2_relu[0][0]        
__________________________________________________________________________________________________
conv2_block2_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block2_3_conv[0][0]        
__________________________________________________________________________________________________
conv2_block2_add (Add)          (None, 56, 56, 256)  0           conv2_block1_out[0][0]           
                                                                 conv2_block2_3_bn[0][0]          
__________________________________________________________________________________________________
conv2_block2_out (Activation)   (None, 56, 56, 256)  0           conv2_block2_add[0][0]           
__________________________________________________________________________________________________
conv2_block3_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block2_out[0][0]           
__________________________________________________________________________________________________
conv2_block3_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block3_1_relu (Activation (None, 56, 56, 64)   0           conv2_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block3_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block3_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv2_block3_2_relu (Activation (None, 56, 56, 64)   0           conv2_block3_2_bn[0][0]          
__________________________________________________________________________________________________
conv2_block3_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block3_2_relu[0][0]        
__________________________________________________________________________________________________
conv2_block3_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block3_3_conv[0][0]        
__________________________________________________________________________________________________
conv2_block3_add (Add)          (None, 56, 56, 256)  0           conv2_block2_out[0][0]           
                                                                 conv2_block3_3_bn[0][0]          
__________________________________________________________________________________________________
conv2_block3_out (Activation)   (None, 56, 56, 256)  0           conv2_block3_add[0][0]           
__________________________________________________________________________________________________
conv3_block1_1_conv (Conv2D)    (None, 28, 28, 128)  32896       conv2_block3_out[0][0]           
__________________________________________________________________________________________________
conv3_block1_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block1_1_relu (Activation (None, 28, 28, 128)  0           conv3_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block1_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block1_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block1_2_relu (Activation (None, 28, 28, 128)  0           conv3_block1_2_bn[0][0]          
__________________________________________________________________________________________________
conv3_block1_0_conv (Conv2D)    (None, 28, 28, 512)  131584      conv2_block3_out[0][0]           
__________________________________________________________________________________________________
conv3_block1_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block1_2_relu[0][0]        
__________________________________________________________________________________________________
conv3_block1_0_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_0_conv[0][0]        
__________________________________________________________________________________________________
conv3_block1_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_3_conv[0][0]        
__________________________________________________________________________________________________
conv3_block1_add (Add)          (None, 28, 28, 512)  0           conv3_block1_0_bn[0][0]          
                                                                 conv3_block1_3_bn[0][0]          
__________________________________________________________________________________________________
conv3_block1_out (Activation)   (None, 28, 28, 512)  0           conv3_block1_add[0][0]           
__________________________________________________________________________________________________
conv3_block2_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block1_out[0][0]           
__________________________________________________________________________________________________
conv3_block2_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block2_1_relu (Activation (None, 28, 28, 128)  0           conv3_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block2_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block2_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block2_2_relu (Activation (None, 28, 28, 128)  0           conv3_block2_2_bn[0][0]          
__________________________________________________________________________________________________
conv3_block2_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block2_2_relu[0][0]        
__________________________________________________________________________________________________
conv3_block2_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block2_3_conv[0][0]        
__________________________________________________________________________________________________
conv3_block2_add (Add)          (None, 28, 28, 512)  0           conv3_block1_out[0][0]           
                                                                 conv3_block2_3_bn[0][0]          
__________________________________________________________________________________________________
conv3_block2_out (Activation)   (None, 28, 28, 512)  0           conv3_block2_add[0][0]           
__________________________________________________________________________________________________
conv3_block3_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block2_out[0][0]           
__________________________________________________________________________________________________
conv3_block3_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block3_1_relu (Activation (None, 28, 28, 128)  0           conv3_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block3_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block3_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block3_2_relu (Activation (None, 28, 28, 128)  0           conv3_block3_2_bn[0][0]          
__________________________________________________________________________________________________
conv3_block3_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block3_2_relu[0][0]        
__________________________________________________________________________________________________
conv3_block3_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block3_3_conv[0][0]        
__________________________________________________________________________________________________
conv3_block3_add (Add)          (None, 28, 28, 512)  0           conv3_block2_out[0][0]           
                                                                 conv3_block3_3_bn[0][0]          
__________________________________________________________________________________________________
conv3_block3_out (Activation)   (None, 28, 28, 512)  0           conv3_block3_add[0][0]           
__________________________________________________________________________________________________
conv3_block4_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block3_out[0][0]           
__________________________________________________________________________________________________
conv3_block4_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block4_1_relu (Activation (None, 28, 28, 128)  0           conv3_block4_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block4_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block4_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block4_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block4_2_relu (Activation (None, 28, 28, 128)  0           conv3_block4_2_bn[0][0]          
__________________________________________________________________________________________________
conv3_block4_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block4_2_relu[0][0]        
__________________________________________________________________________________________________
conv3_block4_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block4_3_conv[0][0]        
__________________________________________________________________________________________________
conv3_block4_add (Add)          (None, 28, 28, 512)  0           conv3_block3_out[0][0]           
                                                                 conv3_block4_3_bn[0][0]          
__________________________________________________________________________________________________
conv3_block4_out (Activation)   (None, 28, 28, 512)  0           conv3_block4_add[0][0]           
__________________________________________________________________________________________________
conv4_block1_1_conv (Conv2D)    (None, 14, 14, 256)  131328      conv3_block4_out[0][0]           
__________________________________________________________________________________________________
conv4_block1_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block1_1_relu (Activation (None, 14, 14, 256)  0           conv4_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block1_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block1_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block1_2_relu (Activation (None, 14, 14, 256)  0           conv4_block1_2_bn[0][0]          
__________________________________________________________________________________________________
conv4_block1_0_conv (Conv2D)    (None, 14, 14, 1024) 525312      conv3_block4_out[0][0]           
__________________________________________________________________________________________________
conv4_block1_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block1_2_relu[0][0]        
__________________________________________________________________________________________________
conv4_block1_0_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_0_conv[0][0]        
__________________________________________________________________________________________________
conv4_block1_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_3_conv[0][0]        
__________________________________________________________________________________________________
conv4_block1_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_0_bn[0][0]          
                                                                 conv4_block1_3_bn[0][0]          
__________________________________________________________________________________________________
conv4_block1_out (Activation)   (None, 14, 14, 1024) 0           conv4_block1_add[0][0]           
__________________________________________________________________________________________________
conv4_block2_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block1_out[0][0]           
__________________________________________________________________________________________________
conv4_block2_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block2_1_relu (Activation (None, 14, 14, 256)  0           conv4_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block2_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block2_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block2_2_relu (Activation (None, 14, 14, 256)  0           conv4_block2_2_bn[0][0]          
__________________________________________________________________________________________________
conv4_block2_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block2_2_relu[0][0]        
__________________________________________________________________________________________________
conv4_block2_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block2_3_conv[0][0]        
__________________________________________________________________________________________________
conv4_block2_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_out[0][0]           
                                                                 conv4_block2_3_bn[0][0]          
__________________________________________________________________________________________________
conv4_block2_out (Activation)   (None, 14, 14, 1024) 0           conv4_block2_add[0][0]           
__________________________________________________________________________________________________
conv4_block3_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block2_out[0][0]           
__________________________________________________________________________________________________
conv4_block3_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block3_1_relu (Activation (None, 14, 14, 256)  0           conv4_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block3_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block3_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block3_2_relu (Activation (None, 14, 14, 256)  0           conv4_block3_2_bn[0][0]          
__________________________________________________________________________________________________
conv4_block3_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block3_2_relu[0][0]        
__________________________________________________________________________________________________
conv4_block3_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block3_3_conv[0][0]        
__________________________________________________________________________________________________
conv4_block3_add (Add)          (None, 14, 14, 1024) 0           conv4_block2_out[0][0]           
                                                                 conv4_block3_3_bn[0][0]          
__________________________________________________________________________________________________
conv4_block3_out (Activation)   (None, 14, 14, 1024) 0           conv4_block3_add[0][0]           
__________________________________________________________________________________________________
conv4_block4_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block3_out[0][0]           
__________________________________________________________________________________________________
conv4_block4_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block4_1_relu (Activation (None, 14, 14, 256)  0           conv4_block4_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block4_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block4_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block4_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block4_2_relu (Activation (None, 14, 14, 256)  0           conv4_block4_2_bn[0][0]          
__________________________________________________________________________________________________
conv4_block4_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block4_2_relu[0][0]        
__________________________________________________________________________________________________
conv4_block4_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block4_3_conv[0][0]        
__________________________________________________________________________________________________
conv4_block4_add (Add)          (None, 14, 14, 1024) 0           conv4_block3_out[0][0]           
                                                                 conv4_block4_3_bn[0][0]          
__________________________________________________________________________________________________
conv4_block4_out (Activation)   (None, 14, 14, 1024) 0           conv4_block4_add[0][0]           
__________________________________________________________________________________________________
conv4_block5_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block4_out[0][0]           
__________________________________________________________________________________________________
conv4_block5_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block5_1_relu (Activation (None, 14, 14, 256)  0           conv4_block5_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block5_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block5_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block5_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block5_2_relu (Activation (None, 14, 14, 256)  0           conv4_block5_2_bn[0][0]          
__________________________________________________________________________________________________
conv4_block5_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block5_2_relu[0][0]        
__________________________________________________________________________________________________
conv4_block5_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block5_3_conv[0][0]        
__________________________________________________________________________________________________
conv4_block5_add (Add)          (None, 14, 14, 1024) 0           conv4_block4_out[0][0]           
                                                                 conv4_block5_3_bn[0][0]          
__________________________________________________________________________________________________
conv4_block5_out (Activation)   (None, 14, 14, 1024) 0           conv4_block5_add[0][0]           
__________________________________________________________________________________________________
conv4_block6_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block5_out[0][0]           
__________________________________________________________________________________________________
conv4_block6_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block6_1_relu (Activation (None, 14, 14, 256)  0           conv4_block6_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block6_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block6_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block6_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block6_2_relu (Activation (None, 14, 14, 256)  0           conv4_block6_2_bn[0][0]          
__________________________________________________________________________________________________
conv4_block6_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block6_2_relu[0][0]        
__________________________________________________________________________________________________
conv4_block6_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block6_3_conv[0][0]        
__________________________________________________________________________________________________
conv4_block6_add (Add)          (None, 14, 14, 1024) 0           conv4_block5_out[0][0]           
                                                                 conv4_block6_3_bn[0][0]          
__________________________________________________________________________________________________
conv4_block6_out (Activation)   (None, 14, 14, 1024) 0           conv4_block6_add[0][0]           
__________________________________________________________________________________________________
conv5_block1_1_conv (Conv2D)    (None, 7, 7, 512)    524800      conv4_block6_out[0][0]           
__________________________________________________________________________________________________
conv5_block1_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block1_1_relu (Activation (None, 7, 7, 512)    0           conv5_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block1_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block1_2_relu (Activation (None, 7, 7, 512)    0           conv5_block1_2_bn[0][0]          
__________________________________________________________________________________________________
conv5_block1_0_conv (Conv2D)    (None, 7, 7, 2048)   2099200     conv4_block6_out[0][0]           
__________________________________________________________________________________________________
conv5_block1_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block1_2_relu[0][0]        
__________________________________________________________________________________________________
conv5_block1_0_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_0_conv[0][0]        
__________________________________________________________________________________________________
conv5_block1_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_3_conv[0][0]        
__________________________________________________________________________________________________
conv5_block1_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_0_bn[0][0]          
                                                                 conv5_block1_3_bn[0][0]          
__________________________________________________________________________________________________
conv5_block1_out (Activation)   (None, 7, 7, 2048)   0           conv5_block1_add[0][0]           
__________________________________________________________________________________________________
conv5_block2_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block1_out[0][0]           
__________________________________________________________________________________________________
conv5_block2_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block2_1_relu (Activation (None, 7, 7, 512)    0           conv5_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block2_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block2_2_relu (Activation (None, 7, 7, 512)    0           conv5_block2_2_bn[0][0]          
__________________________________________________________________________________________________
conv5_block2_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block2_2_relu[0][0]        
__________________________________________________________________________________________________
conv5_block2_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block2_3_conv[0][0]        
__________________________________________________________________________________________________
conv5_block2_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_out[0][0]           
                                                                 conv5_block2_3_bn[0][0]          
__________________________________________________________________________________________________
conv5_block2_out (Activation)   (None, 7, 7, 2048)   0           conv5_block2_add[0][0]           
__________________________________________________________________________________________________
conv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block2_out[0][0]           
__________________________________________________________________________________________________
conv5_block3_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[0][0]          
__________________________________________________________________________________________________
conv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[0][0]        
__________________________________________________________________________________________________
conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block3_3_conv[0][0]        
__________________________________________________________________________________________________
conv5_block3_add (Add)          (None, 7, 7, 2048)   0           conv5_block2_out[0][0]           
                                                                 conv5_block3_3_bn[0][0]          
__________________________________________________________________________________________________
conv5_block3_out (Activation)   (None, 7, 7, 2048)   0           conv5_block3_add[0][0]           
__________________________________________________________________________________________________
flatten (Flatten)               (None, 100352)       0           conv5_block3_out[0][0]           
==================================================================================================
Total params: 23,587,712
Trainable params: 0
Non-trainable params: 23,587,712
__________________________________________________________________________________________________

# creation of the model, using the resnet architecture and adding my own classes after the flattening layer
model = Sequential()
model.add(resnet)
model.add(Dense(512, activation='relu', input_dim= (224, 224, 3)))
model.add(Dropout(0.3))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(num_classes, activation='softmax'))

# summary of the model
model.summary()

# specifying a learning rate to optimizer adam
opt = tf.keras.optimizers.Adam(lr=0.000001)
# compiling the model
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['acc'])

# Fit the model
history = model.fit(X, Y, validation_split=0.40, epochs=25, batch_size=32, verbose=1)

# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.show()

# summarize history for accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.show()

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
model (Functional)           (None, 100352)            23587712  
_________________________________________________________________
dense (Dense)                (None, 512)               51380736  
_________________________________________________________________
dropout (Dropout)            (None, 512)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 512)               262656    
_________________________________________________________________
dropout_1 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 8)                 4104      
=================================================================
Total params: 75,235,208
Trainable params: 51,647,496
Non-trainable params: 23,587,712
_________________________________________________________________
Epoch 1/25
112/112 [==============================] - 55s 181ms/step - loss: 2.4391 - acc: 0.1262 - val_loss: 1.7469 - val_acc: 0.2800
Epoch 2/25
112/112 [==============================] - 18s 162ms/step - loss: 1.9399 - acc: 0.2149 - val_loss: 1.7279 - val_acc: 0.2473
Epoch 3/25
112/112 [==============================] - 18s 163ms/step - loss: 1.8573 - acc: 0.2442 - val_loss: 1.7157 - val_acc: 0.2728
Epoch 4/25
112/112 [==============================] - 18s 164ms/step - loss: 1.8452 - acc: 0.2318 - val_loss: 1.7133 - val_acc: 0.2795
Epoch 5/25
112/112 [==============================] - 18s 165ms/step - loss: 1.8142 - acc: 0.2327 - val_loss: 1.7124 - val_acc: 0.2804
Epoch 6/25
112/112 [==============================] - 19s 166ms/step - loss: 1.8217 - acc: 0.2276 - val_loss: 1.7082 - val_acc: 0.2812
Epoch 7/25
112/112 [==============================] - 19s 167ms/step - loss: 1.7848 - acc: 0.2518 - val_loss: 1.7087 - val_acc: 0.3290
Epoch 8/25
112/112 [==============================] - 19s 168ms/step - loss: 1.7945 - acc: 0.2471 - val_loss: 1.7044 - val_acc: 0.2800
Epoch 9/25
112/112 [==============================] - 19s 168ms/step - loss: 1.7674 - acc: 0.2619 - val_loss: 1.7059 - val_acc: 0.3273
Epoch 10/25
112/112 [==============================] - 19s 169ms/step - loss: 1.7900 - acc: 0.2303 - val_loss: 1.7031 - val_acc: 0.2787
Epoch 11/25
112/112 [==============================] - 19s 169ms/step - loss: 1.7705 - acc: 0.2577 - val_loss: 1.7006 - val_acc: 0.2800
Epoch 12/25
112/112 [==============================] - 19s 170ms/step - loss: 1.7762 - acc: 0.2461 - val_loss: 1.6999 - val_acc: 0.2804
Epoch 13/25
112/112 [==============================] - 19s 170ms/step - loss: 1.7725 - acc: 0.2532 - val_loss: 1.6980 - val_acc: 0.2804
Epoch 14/25
112/112 [==============================] - 19s 171ms/step - loss: 1.7765 - acc: 0.2481 - val_loss: 1.6972 - val_acc: 0.2791
Epoch 15/25
112/112 [==============================] - 19s 171ms/step - loss: 1.7618 - acc: 0.2573 - val_loss: 1.6956 - val_acc: 0.2795
Epoch 16/25
112/112 [==============================] - 19s 171ms/step - loss: 1.7400 - acc: 0.2809 - val_loss: 1.6941 - val_acc: 0.3001
Epoch 17/25
112/112 [==============================] - 19s 171ms/step - loss: 1.7483 - acc: 0.2575 - val_loss: 1.6920 - val_acc: 0.2787
Epoch 18/25
112/112 [==============================] - 19s 171ms/step - loss: 1.7681 - acc: 0.2658 - val_loss: 1.6906 - val_acc: 0.2804
Epoch 19/25
112/112 [==============================] - 19s 171ms/step - loss: 1.7382 - acc: 0.2836 - val_loss: 1.6896 - val_acc: 0.2791
Epoch 20/25
112/112 [==============================] - 19s 171ms/step - loss: 1.7472 - acc: 0.2703 - val_loss: 1.6905 - val_acc: 0.3755
Epoch 21/25
112/112 [==============================] - 19s 171ms/step - loss: 1.7364 - acc: 0.2676 - val_loss: 1.6838 - val_acc: 0.2795
Epoch 22/25
112/112 [==============================] - 19s 171ms/step - loss: 1.7403 - acc: 0.2662 - val_loss: 1.6829 - val_acc: 0.2791
Epoch 23/25
112/112 [==============================] - 19s 171ms/step - loss: 1.7370 - acc: 0.2681 - val_loss: 1.6795 - val_acc: 0.2808
Epoch 24/25
112/112 [==============================] - 19s 171ms/step - loss: 1.7278 - acc: 0.2759 - val_loss: 1.6787 - val_acc: 0.2804
Epoch 25/25
112/112 [==============================] - 19s 171ms/step - loss: 1.7345 - acc: 0.2666 - val_loss: 1.6791 - val_acc: 0.2791

# creation of the model, using the resnet architecture and adding my own classes after the flattening layer
model2 = Sequential()
model2.add(resnet)
model2.add(Dense(512, activation='relu', input_dim= (224, 224, 3)))
model2.add(Dense(512, activation='relu'))
model2.add(Dense(num_classes, activation='softmax'))

# summary of the model
model2.summary()

# specifying a learning rate to optimizer adam
opt = tf.keras.optimizers.Adam(lr=0.0001)
# compiling the model
model2.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['acc'])

# Fit the model
history = model2.fit(X, Y, validation_split=0.33, epochs=50, batch_size=64, verbose=1)

# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.show()

# summarize history for accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.show()

Model: "sequential_8"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
model (Functional)           (None, 100352)            23587712  
_________________________________________________________________
dense_29 (Dense)             (None, 512)               51380736  
_________________________________________________________________
dense_30 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_31 (Dense)             (None, 8)                 4104      
=================================================================
Total params: 75,235,208
Trainable params: 51,647,496
Non-trainable params: 23,587,712
_________________________________________________________________
Epoch 1/50
63/63 [==============================] - 23s 322ms/step - loss: 1.8961 - acc: 0.2459 - val_loss: 1.6890 - val_acc: 0.3028
Epoch 2/50
63/63 [==============================] - 19s 297ms/step - loss: 1.7277 - acc: 0.2960 - val_loss: 1.5966 - val_acc: 0.3537
Epoch 3/50
63/63 [==============================] - 18s 289ms/step - loss: 1.6647 - acc: 0.3226 - val_loss: 1.5962 - val_acc: 0.2942
Epoch 4/50
63/63 [==============================] - 18s 291ms/step - loss: 1.6198 - acc: 0.3434 - val_loss: 1.5757 - val_acc: 0.2952
Epoch 5/50
63/63 [==============================] - 18s 294ms/step - loss: 1.5756 - acc: 0.3892 - val_loss: 1.5443 - val_acc: 0.3821
Epoch 6/50
63/63 [==============================] - 19s 296ms/step - loss: 1.5550 - acc: 0.3885 - val_loss: 1.5385 - val_acc: 0.5147
Epoch 7/50
63/63 [==============================] - 18s 295ms/step - loss: 1.5324 - acc: 0.4158 - val_loss: 1.4837 - val_acc: 0.4192
Epoch 8/50
63/63 [==============================] - 18s 293ms/step - loss: 1.4787 - acc: 0.4370 - val_loss: 1.4514 - val_acc: 0.4507
Epoch 9/50
63/63 [==============================] - 18s 293ms/step - loss: 1.4657 - acc: 0.4478 - val_loss: 1.3822 - val_acc: 0.5137
Epoch 10/50
63/63 [==============================] - 18s 295ms/step - loss: 1.3718 - acc: 0.5079 - val_loss: 1.5005 - val_acc: 0.4365
Epoch 11/50
63/63 [==============================] - 18s 294ms/step - loss: 1.4599 - acc: 0.4315 - val_loss: 1.3274 - val_acc: 0.5371
Epoch 12/50
63/63 [==============================] - 18s 294ms/step - loss: 1.3166 - acc: 0.5303 - val_loss: 1.3310 - val_acc: 0.4893
Epoch 13/50
63/63 [==============================] - 18s 293ms/step - loss: 1.2977 - acc: 0.5420 - val_loss: 1.2649 - val_acc: 0.5676
Epoch 14/50
63/63 [==============================] - 18s 293ms/step - loss: 1.2627 - acc: 0.5650 - val_loss: 1.2257 - val_acc: 0.6057
Epoch 15/50
63/63 [==============================] - 18s 294ms/step - loss: 1.2643 - acc: 0.5646 - val_loss: 1.2266 - val_acc: 0.5513
Epoch 16/50
63/63 [==============================] - 18s 294ms/step - loss: 1.2112 - acc: 0.5595 - val_loss: 1.2078 - val_acc: 0.5589
Epoch 17/50
63/63 [==============================] - 18s 294ms/step - loss: 1.2148 - acc: 0.5626 - val_loss: 1.2276 - val_acc: 0.6016
Epoch 18/50
63/63 [==============================] - 18s 295ms/step - loss: 1.1910 - acc: 0.5771 - val_loss: 1.2923 - val_acc: 0.5772
Epoch 19/50
63/63 [==============================] - 18s 294ms/step - loss: 1.1599 - acc: 0.6052 - val_loss: 1.2850 - val_acc: 0.4558
Epoch 20/50
63/63 [==============================] - 18s 294ms/step - loss: 1.1581 - acc: 0.5869 - val_loss: 1.1593 - val_acc: 0.5706
Epoch 21/50
63/63 [==============================] - 18s 294ms/step - loss: 1.0972 - acc: 0.6180 - val_loss: 1.0588 - val_acc: 0.6484
Epoch 22/50
63/63 [==============================] - 18s 295ms/step - loss: 1.0492 - acc: 0.6390 - val_loss: 1.0424 - val_acc: 0.6621
Epoch 23/50
63/63 [==============================] - 18s 294ms/step - loss: 1.0525 - acc: 0.6223 - val_loss: 1.0381 - val_acc: 0.6280
Epoch 24/50
63/63 [==============================] - 18s 294ms/step - loss: 0.9968 - acc: 0.6529 - val_loss: 1.0017 - val_acc: 0.6768
Epoch 25/50
63/63 [==============================] - 18s 294ms/step - loss: 1.0197 - acc: 0.6475 - val_loss: 1.0528 - val_acc: 0.6458
Epoch 26/50
63/63 [==============================] - 18s 295ms/step - loss: 0.9971 - acc: 0.6633 - val_loss: 1.0629 - val_acc: 0.6341
Epoch 27/50
63/63 [==============================] - 18s 294ms/step - loss: 1.0670 - acc: 0.6175 - val_loss: 1.0681 - val_acc: 0.6321
Epoch 28/50
63/63 [==============================] - 18s 293ms/step - loss: 1.0095 - acc: 0.6497 - val_loss: 0.9813 - val_acc: 0.6773
Epoch 29/50
63/63 [==============================] - 18s 294ms/step - loss: 0.9365 - acc: 0.6759 - val_loss: 1.1566 - val_acc: 0.5523
Epoch 30/50
63/63 [==============================] - 18s 294ms/step - loss: 0.9931 - acc: 0.6404 - val_loss: 1.0098 - val_acc: 0.6631
Epoch 31/50
63/63 [==============================] - 18s 294ms/step - loss: 0.9619 - acc: 0.6594 - val_loss: 0.9506 - val_acc: 0.6768
Epoch 32/50
63/63 [==============================] - 18s 294ms/step - loss: 0.9246 - acc: 0.6754 - val_loss: 0.9254 - val_acc: 0.6794
Epoch 33/50
63/63 [==============================] - 18s 294ms/step - loss: 0.8649 - acc: 0.7050 - val_loss: 0.9705 - val_acc: 0.6753
Epoch 34/50
63/63 [==============================] - 18s 294ms/step - loss: 0.9154 - acc: 0.6726 - val_loss: 0.9514 - val_acc: 0.6596
Epoch 35/50
63/63 [==============================] - 18s 294ms/step - loss: 0.8860 - acc: 0.6828 - val_loss: 1.0110 - val_acc: 0.6590
Epoch 36/50
63/63 [==============================] - 18s 294ms/step - loss: 0.8426 - acc: 0.7078 - val_loss: 0.9094 - val_acc: 0.6845
Epoch 37/50
63/63 [==============================] - 18s 293ms/step - loss: 0.8736 - acc: 0.6833 - val_loss: 0.9704 - val_acc: 0.6174
Epoch 38/50
63/63 [==============================] - 18s 294ms/step - loss: 0.9086 - acc: 0.6695 - val_loss: 0.9207 - val_acc: 0.6733
Epoch 39/50
63/63 [==============================] - 18s 293ms/step - loss: 0.9031 - acc: 0.6687 - val_loss: 0.8839 - val_acc: 0.6784
Epoch 40/50
63/63 [==============================] - 18s 293ms/step - loss: 0.8068 - acc: 0.7246 - val_loss: 0.8938 - val_acc: 0.6936
Epoch 41/50
63/63 [==============================] - 18s 294ms/step - loss: 0.7926 - acc: 0.7334 - val_loss: 1.0103 - val_acc: 0.6555
Epoch 42/50
63/63 [==============================] - 18s 294ms/step - loss: 0.9714 - acc: 0.6519 - val_loss: 0.8403 - val_acc: 0.7119
Epoch 43/50
63/63 [==============================] - 18s 295ms/step - loss: 0.8055 - acc: 0.7177 - val_loss: 0.7915 - val_acc: 0.7378
Epoch 44/50
63/63 [==============================] - 18s 294ms/step - loss: 0.7743 - acc: 0.7343 - val_loss: 0.8281 - val_acc: 0.7231
Epoch 45/50
63/63 [==============================] - 18s 294ms/step - loss: 0.8184 - acc: 0.6957 - val_loss: 0.7949 - val_acc: 0.7297
Epoch 46/50
63/63 [==============================] - 18s 293ms/step - loss: 0.8232 - acc: 0.7074 - val_loss: 0.8408 - val_acc: 0.7073
Epoch 47/50
63/63 [==============================] - 18s 293ms/step - loss: 0.7374 - acc: 0.7439 - val_loss: 0.7834 - val_acc: 0.7282
Epoch 48/50
63/63 [==============================] - 18s 293ms/step - loss: 0.7603 - acc: 0.7296 - val_loss: 0.8920 - val_acc: 0.6885
Epoch 49/50
63/63 [==============================] - 18s 294ms/step - loss: 0.7805 - acc: 0.7287 - val_loss: 0.8041 - val_acc: 0.7180
Epoch 50/50
63/63 [==============================] - 18s 294ms/step - loss: 0.7500 - acc: 0.7274 - val_loss: 0.7907 - val_acc: 0.7195

# creation of the model, using the resnet architecture and adding my own classes after the flattening layer
model3 = Sequential()
model3.add(resnet)
model3.add(Dense(512, activation='relu', input_dim= (224, 224, 3)))
model3.add(Dense(512, activation='relu'))
model3.add(Dense(256, activation='relu'))
model3.add(Dense(128, activation='relu'))
model3.add(Dense(num_classes, activation='softmax'))

# summary of the model
model3.summary()

# specifying a learning rate to optimizer adam
opt = tf.keras.optimizers.Adam(lr=0.001)
# compiling the model
model3.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['acc'])

# Fit the model
history = model3.fit(X, Y, validation_split=0.33, epochs=30, batch_size=128, verbose=1)

# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.show()

# summarize history for accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.show()

Model: "sequential_7"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
model (Functional)           (None, 100352)            23587712  
_________________________________________________________________
dense_24 (Dense)             (None, 512)               51380736  
_________________________________________________________________
dense_25 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_26 (Dense)             (None, 256)               131328    
_________________________________________________________________
dense_27 (Dense)             (None, 128)               32896     
_________________________________________________________________
dense_28 (Dense)             (None, 8)                 1032      
=================================================================
Total params: 75,396,360
Trainable params: 51,808,648
Non-trainable params: 23,587,712
_________________________________________________________________
Epoch 1/30
125/125 [==============================] - 23s 162ms/step - loss: 3.3306 - acc: 0.2265 - val_loss: 1.8907 - val_acc: 0.0965
Epoch 2/30
125/125 [==============================] - 19s 155ms/step - loss: 1.7635 - acc: 0.2515 - val_loss: 1.6775 - val_acc: 0.2444
Epoch 3/30
125/125 [==============================] - 19s 156ms/step - loss: 1.7005 - acc: 0.2764 - val_loss: 1.6804 - val_acc: 0.3359
Epoch 4/30
125/125 [==============================] - 19s 156ms/step - loss: 1.6895 - acc: 0.2943 - val_loss: 1.5737 - val_acc: 0.3933
Epoch 5/30
125/125 [==============================] - 19s 156ms/step - loss: 1.6437 - acc: 0.3126 - val_loss: 1.6559 - val_acc: 0.2607
Epoch 6/30
125/125 [==============================] - 19s 156ms/step - loss: 1.6046 - acc: 0.3256 - val_loss: 1.6319 - val_acc: 0.3755
Epoch 7/30
125/125 [==============================] - 19s 156ms/step - loss: 1.5856 - acc: 0.3712 - val_loss: 1.5136 - val_acc: 0.4014
Epoch 8/30
125/125 [==============================] - 19s 156ms/step - loss: 1.6163 - acc: 0.3115 - val_loss: 1.5408 - val_acc: 0.3379
Epoch 9/30
125/125 [==============================] - 19s 156ms/step - loss: 1.6050 - acc: 0.3328 - val_loss: 1.5086 - val_acc: 0.4228
Epoch 10/30
125/125 [==============================] - 19s 156ms/step - loss: 1.5208 - acc: 0.3971 - val_loss: 1.4521 - val_acc: 0.4177
Epoch 11/30
125/125 [==============================] - 19s 156ms/step - loss: 1.5249 - acc: 0.3759 - val_loss: 1.4317 - val_acc: 0.4284
Epoch 12/30
125/125 [==============================] - 19s 156ms/step - loss: 1.5959 - acc: 0.3540 - val_loss: 1.4076 - val_acc: 0.4411
Epoch 13/30
125/125 [==============================] - 19s 156ms/step - loss: 1.4776 - acc: 0.3968 - val_loss: 1.5498 - val_acc: 0.3511
Epoch 14/30
125/125 [==============================] - 19s 156ms/step - loss: 1.5445 - acc: 0.3452 - val_loss: 1.4903 - val_acc: 0.3623
Epoch 15/30
125/125 [==============================] - 19s 156ms/step - loss: 1.5079 - acc: 0.3845 - val_loss: 1.5028 - val_acc: 0.3577
Epoch 16/30
125/125 [==============================] - 19s 156ms/step - loss: 1.5198 - acc: 0.3656 - val_loss: 1.4436 - val_acc: 0.4055
Epoch 17/30
125/125 [==============================] - 19s 156ms/step - loss: 1.4661 - acc: 0.4017 - val_loss: 1.4692 - val_acc: 0.4319
Epoch 18/30
125/125 [==============================] - 19s 156ms/step - loss: 1.4541 - acc: 0.4191 - val_loss: 1.4589 - val_acc: 0.4304
Epoch 19/30
125/125 [==============================] - 19s 156ms/step - loss: 1.4464 - acc: 0.4090 - val_loss: 1.3845 - val_acc: 0.4507
Epoch 20/30
125/125 [==============================] - 19s 156ms/step - loss: 1.6255 - acc: 0.3409 - val_loss: 1.4332 - val_acc: 0.4177
Epoch 21/30
125/125 [==============================] - 20s 157ms/step - loss: 1.4836 - acc: 0.3810 - val_loss: 1.4319 - val_acc: 0.3857
Epoch 22/30
125/125 [==============================] - 19s 156ms/step - loss: 1.4285 - acc: 0.4171 - val_loss: 1.3591 - val_acc: 0.4705
Epoch 23/30
125/125 [==============================] - 20s 156ms/step - loss: 1.4351 - acc: 0.4124 - val_loss: 1.3783 - val_acc: 0.4446
Epoch 24/30
125/125 [==============================] - 19s 156ms/step - loss: 1.3792 - acc: 0.4145 - val_loss: 1.3742 - val_acc: 0.4512
Epoch 25/30
125/125 [==============================] - 19s 156ms/step - loss: 1.4214 - acc: 0.4150 - val_loss: 1.3555 - val_acc: 0.4583
Epoch 26/30
125/125 [==============================] - 19s 156ms/step - loss: 1.3855 - acc: 0.4190 - val_loss: 1.3789 - val_acc: 0.4543
Epoch 27/30
125/125 [==============================] - 19s 156ms/step - loss: 1.3519 - acc: 0.4566 - val_loss: 1.3091 - val_acc: 0.4842
Epoch 28/30
125/125 [==============================] - 19s 156ms/step - loss: 1.3783 - acc: 0.4218 - val_loss: 1.5186 - val_acc: 0.3740
Epoch 29/30
125/125 [==============================] - 19s 156ms/step - loss: 1.3650 - acc: 0.4486 - val_loss: 1.7167 - val_acc: 0.2708
Epoch 30/30
125/125 [==============================] - 19s 156ms/step - loss: 1.4661 - acc: 0.4069 - val_loss: 1.3084 - val_acc: 0.4903

Predictions on the resnet model

Angry

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry1.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry2.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry3.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry4.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry5.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are disgusted

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry6.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry7.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are angry

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry8.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry9.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry10.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry11.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry12.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry13.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry14.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry15.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry16.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry17.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry18.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry19.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Angry/Angry20.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

Disgust

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust1.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust2.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust3.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust4.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust5.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust6.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust7.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust8.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are disgusted

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust9.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust10.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust11.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust12.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust13.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust14.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust15.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust16.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust17.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are disgusted

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust18.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are disgusted

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust19.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Disgust/Disgust20.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

Fear

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Fear/Fear1.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Fear/Fear2.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are disgusted

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Fear/Fear3.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are disgusted

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Fear/Fear4.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Fear/Fear5.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Fear/Fear7.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Fear/Fear8.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Fear/Fear9.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Fear/Fear10.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Fear/Fear11.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Fear/Fear12.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Fear/Fear13.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Fear/Fear14.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Fear/Fear15.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Fear/Fear16.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Fear/Fear17.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Fear/Fear18.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Fear/Fear19.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Fear/Fear20.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

Happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy1.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy2.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy3.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy4.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are disgusted

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy5.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy6.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy7.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy8.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy9.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy10.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are disgusted

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy11.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy12.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy13.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy14.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy15.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy16.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy17.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy18.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy19.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are disgusted

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Happy/Happy20.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

Neutral

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral1.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral2.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral3.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral4.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral5.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral6.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral7.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral8.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral9.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral10.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral11.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral12.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral13.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral14.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral15.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral16.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral17.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral18.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral19.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Neutral/Neutral20.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

Sad

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad1.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad2.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad3.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad4.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad5.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad6.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad7.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad8.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad9.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad10.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad11.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad12.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad13.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad14.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad15.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad16.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad17.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad18.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad19.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Sad/Sad20.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

Surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised1.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised2.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised3.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised4.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised5.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised6.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised7.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised8.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised9.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised10.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised11.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised12.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised13.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised14.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are disgusted

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised15.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised16.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are disgusted

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised17.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised18.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised19.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

you are happy

image1 =  Image.open("/content/drive/MyDrive/images/unclassifiedImages/Surprised/Surprised20.jpg")

plt.imshow(image1)
plt.show()
   
rgbImage = image1.resize((224,224),Image.ANTIALIAS).convert('RGB')
plt.imshow(rgbImage)
plt.show()

Xt = []
Xt.append(np.array(rgbImage))


# Convert to NP array
Xt = np.array(Xt)

# Reshape 2D
Xt = Xt.reshape(Xt.shape[0], 224, 224, 3).astype('float32')

# Normalize the data
Xt = Xt /255


result = model2.predict_classes(Xt)
#result = np.argmax(modelC.predict(Xt))

plt.imshow(image1)
plt.show()

if result[0] == 1:
    print("You are angry")
if result[0] == 2:
    print("You are disgusted")
if result[0] == 3:
    print("you are fear")
if result[0] == 4:
    print("you are happy")
if result[0] == 5:
    print("You are netural")
if result[0] == 6:
    print("You are sad")
if result[0] == 7:
    print("You are surprised")

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
  warnings.warn('`model.predict_classes()` is deprecated and '

You are surprised